1. Field of the Invention
The present invention generally relates to computer implemented planning resources and decision support tools and, more particularly, to a tool in which core production planning information is provided to a solver which generates a plan of what (and when) is needed to start internally or purchase externally to meet all customer demands of current interest. The invention generates an intelligent MRP match between existing assets and demands across multiple manufacturing facilities within the boundaries established by the manufacturing specifications and process flows and business policies.
2. Background Description
Within the complexity of semiconductor manufacturing, four related decision areas or tiers can be distinguished based on the time scale of the planning horizon and the apparent width of the opportunity window. To facilitate an understanding of the four decision tiers in semiconductor manufacturing, we will reference the following oven example. FIG. 1 is a diagram associated with this example.
Within a zone of control 10, there is a coater machine 12, a work-in-progress (WIP) queue 14, and an oven set 16. Wafers move around the zone of control in groups of twenty-five called a lot. All wafers in the lot are the same type. Each lot must pass through the oven operation ten times. Each oven set is composed of four ovens or tubes 161, 162, 163, and 164 and one robot 166 to load and unload the oven. It takes about ten minutes to load or unload an oven. The process time in the oven depends on the iteration. We will assume one lot to an oven at a time. Before a wafer enters into the oven it must be coated by the coater machine 12. The coating process takes twenty minutes. The coating expires in four hours. If the coating expires the wafer must be stripped, cleaned, recoated. This process takes four hours and often generates yield losses.
The first decision tier, strategic scheduling, is driven by the time frame or lead time required for business plan, resource acquisition, and new product introduction. This tier can often be viewed in two parts; very long-term and long-term. Here, decision makers are concerned with a set of problems that are three months to seven years into the future. Issues considered include, but are not limited to, what markets the firm will be in, general availability of tooling and workers, major changes in processes, changes in or risk assessment of demand for existing product, required or expected incremental improvements in the production process, lead times for additional tooling, manpower and planning. In the oven example of FIG. 1, very long-term decisions are made on whether the ovens are necessary to the production process and, if so, the characteristics needed in the oven. Long-term decisions are made about how many ovens to buy. Tools typically used in planning of this scope are models for capacity planning, cost/pricing, investment optimization, and simulations of key business measures.
The second tier, tactical scheduling, deals with problems the company faces in the next week to six months. Estimates are made of yields, cycle times, and binning percentages. Permissible substitutions are identified. Decisions are made about scheduling starts or releases into the manufacturing line (committing available capacity to new starts). Delivery dates are estimated for firm orders, available "outs" by time buckets are estimated for bulk products, and daily going rates for schedule driven product are set. The order/release plan is generated/regenerated. Reschedules are negotiated with or requested by the ultimate customer. In the oven example of FIG. 1, decisions would be made on the daily going rate for different products, allocation of resources between operations, the number of operators to assign, and machine dedication. Tools typically used in the planning and scheduling of this phase are forward schedulers, fast capacity checkers, and optimization of capacity, commits and cost.
The third tier, operational scheduling, deals with the execution and achievement of a weekly plan. Shipments are made. Serviceability levels are measured. Recovery actions are taken. Optimized consumption of capacity and output of product computed. Tools typically use in support of daily activities are decision support, recovery models, prioritization techniques and deterministic forward schedulers. Manufacturing Execution Systems (MES) are used for floor communications and control. In the oven example of FIG. 1, priorities would be placed on each lot arriving at the ovens based on their relevance to current plan or record. If the ovens "go down" their priority in the repair cue would be set by decisions made in this tier.
The fourth tier, dispatch scheduling or response system, addresses the problems of the next hour to a few weeks by responding to conditions as they emerge in real time and accommodate variances from availability assumed by systems in the plan creation and commitment phases. Essentially, they instruct the operator what to do next to achieve the current goals of manufacturing. Dispatch scheduling decisions concern monitoring and controlling of the actual manufacturing flow or logistics. Here, decisions are made concerning trade-offs between running test lots for a change in an existing product or a new product and running regular manufacturing lots, lot expiration, prioritizing late lots, positioning preventive maintenance downtime, production of similar product to reduce setup time, down stream needs, simultaneous requests on the same piece of equipment, preferred machines for yield considerations, assigning personnel to machines, covering for absences, and reestablishing steady production flow after a machine has been down. In the oven example of FIG. 1, the question is which lot (if any) is run next when an oven is free. Tools used are rule based dispatchers, short interval schedulers and mechanical Work-In-Progress (WIP) limiting constructions.
Of course, there is overlap and interaction between the four decision tiers, but typically different groups are responsible for different scheduling decisions. For example, maintenance may decide on training for their personnel, on work schedules for their people, preventive maintenance, and what machine to repair next. Finance and each building superintendent may make decisions on capital equipment purchases. Industrial Engineering may have the final say on total manpower, but a building superintendent may do the day-to-day scheduling. Marketing may decide when orders for products can be filled and what schedule commitments to make. For strategic and operational decisions, these groups and their associated decision support tools are loosely coordinated or coupled. Finance only requires an estimate of required new tools from each building to estimate capital purchase. Each building requires an estimate on new tool requirements from the product development people. For dispatch decisions, they must be tightly coupled. Lots only get processed when the appropriate tool, operator, and raw material are available. At dispatch rough estimates are no longer sufficient. If a machine is down maintenance must have the appropriately trained individual available to repair the machine. Manufacturing must have the appropriate mix of tools and workers to produce finished goods on a timely basis. At dispatch the decisions made by various groups must be in synchronization or nothing is produced. A manufacturing facility accommodates this tight coupling in only one of two ways: slack (extra tooling and manpower, long lead times, limited product variation, excess inventory and people, differential quality, brand loyalty, and so forth) or strong information systems to make effective decisions.
Within the first, second and third decision tiers, a major planning activity undertaken by microelectronic firms is matching assets with demands. This activity can be broken into three major types of matching that are used throughout the microelectronics industry to support decision making:
(a) Material Requirements Planning (MRP) type of matching--"Opportunity Identification" or "Wish List". For a given set of demand and a given asset profile what work needs to be accomplished to meet demand. PA1 (b) Projected Supply Planning (PSP). Given a set of assets, manufacturing specifications, and business guidelines this application creates an expected or projected supply picture over the next "t" time units. The user supplied guidelines direct how to flow or flush assets "forward" to some inventory or holding point. PA1 (c) Best Can Do (BCD). Given the current manufacturing condition and a prioritized set of demands which demands can be met in what time frame. BCD generally refers to a large set of demands. A preferred BCD is described in application Ser. No. 08/926,131 (IBM Docket BU9-96-194). PA1 (a) recommended future manufacturing starts (planned manufacturing orders), PA1 (b) recommended new purchase orders, PA1 (c) calculation of "need date" for each WIP lot in the manufacturing line based on when the lot is required to meet customer demand, PA1 (d) new purchase orders and recommended alterations is to existing purchase orders guided by user set rules, PA1 (e) recommended interplant shipments in a multi-site environment, and PA1 (f) recommended substitutions.
Arguably, the oldest type of matching is Material Requirements Planning (MRP). MRP is a system for translating demand for final products into specific raw material and manufacturing activity requirements by exploding demand backwards through the bill of material (BOM) and assets. Many authors have published papers and books on MRP. For example, Joseph Orlickly wrote Material Requirements Planning, published by McGraw-Hill, which has become a standard reference. As practiced in the microelectronics industry, MRP systems operate at a specific part number and inventory holding point level of detail.
A difficulty with traditional MRP logic is that it does not adequately handle binned parts. Multiple integrated circuit chips, such as microprocessors, are manufactured on a single semiconductor wafer and separated into individual chips by dicing the wafer. Although all chips are manufactured on a single wafer, testing each chip will reveal that there are variances in the performances of the chips. Assume that the microprocessors are designed for a particular clock speed, say 200 MHz. However, testing the chips shows that there is only a 50% yield of chips meeting this criteria. Of the remaining 50% of the chips, some may perform at a slower but still fast clock speed, say 175 MHz, and others at a still slower clock speed, say 150 MHz. Traditional MRP vastly overstates the required wafer starts needed to meet demand in binning situations. However, for some time an optimization model has been known which minimizes wafer starts in binning situations. Traditionally, this optimization was used in a stand alone form or within another optimization routine.
The "Matching Assets With Demands" problems in semiconductor manufacturing are further complicated by the presence of alternate processes each with its own characteristics (date effective bill of material, yields., cycle times, binning percentages), permissible part number substitutions, and lot-sizing considerations, among others. The proper decisions on which processes to use and which substitutions to make depend upon the state of Work-In-Process (WIP) and on-hand inventory throughout the bills of material supply chains as well as the date-effective cycle times, yields, and binning percentages. Existing heuristic decision technologies make decisions one bills of material level (or at most two levels) at a time thereby ignoring the impact of inventories, cycle times, yields, and binning percentages further down the supply chain. Consequently, the decisions resulting from heuristic technology applied to this environment tend to be poor. One skilled in the art might think of Linear Programming (LP) technology as a means of making optimal decisions considering the global picture. Such technology is effective in terms of considering the full supply chain inventories, cycle times, yields, and binning percentages. However, Linear Programs tend to be slow. Furthermore, lot-sizing poses a challenge for optimization technology such as Linear Programming. Lot-sizing typically requires each production start to have a release quantity of no less than a specified minimum quantity, no more than a specified maximum quantity, and also a multiple of a third specified quantity. Such considerations are non-linear and thus are only modeled in current optimization technology by introducing integer variables. The result would be an Integer Program which would take much longer to execute than the (hardly fast) Linear Program. In practice, this makes Integer Programs unacceptably slow when tackling the scale of most matching problems found in microelectronics.
The solution we propose utilizes the best elements of the Linear Programming (LP) and heuristic technologies and integrates those elements synergistically in a cohesive fashion. This solution utilizes LP technology only where it is needed (to make process and substitution decisions) and utilizes the (faster) heuristic technology to address lot-sizing and other non-linear factors. The resulting matching decisions are more intelligent than those found under other matching methods and can be obtained in a reasonable period of time.